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Visual summarization addresses the task of selecting images from an image collection, so that the sampled images would contain representative information which sufficiently highlights the collected visual data. In this paper, we solve the problem of style-centric visual summarization using photographic landmark images of a city. Different from existing works which typically retrieve landmark images based on salient visual appearances, our proposed method is able to produce different sets of summarized images, while each set corresponds to a particular image style. This is achieved by performing unsupervised clustering on images within and across landmark categories, which discovers the common photographic styles from the input image collection. Our experiments will confirm that, compared to standard clustering algorithms, our approach is able to achieve satisfactory summarization outputs with style consistency.
This paper considers a two-cell uplink cochannel multiple-input multiple-output (MIMO) network with users sequentially arriving to the network. We study the problem of sequential base station (BS) selection for the users, with the selection criterion based on the degrees of freedom (DoF) available for the new arriving user. We find that different sequential BS selections affect individual and network performance in terms of the individual and network sum DoF as well as the number of admissible users in the network. We propose a method to build the tree structure for sequential BS selection, which carries trellis information for individual and system-wide selections. The properties of the tree are analytically studied. It turns out that by adopting an interference coordination strategy based on the concept of interference alignment, a better individual and network performance can be achieved. Simulation compares the proposed DoF-based BS selection and traditional BS selection schemes and highlights the advantages of the proposed scheme.
Mobile applications will become progressively more complicated and diverse. Heterogeneous computing architectures like big.LITTLE are a hardware solution that allows mobile devices to combine computing performance and energy efficiency. However, software solutions that conform to the paradigm of conventional fair scheduling and governing are not applicable to mobile systems, thereby degrading user experience or reducing energy efficiency. In this article, we exploit the concept of application sensitivity, which reflects the user’s attention on each application, and devise a user-centric scheduler and governor that allocate computing resources to applications according to their sensitivity. Furthermore, we integrate our design into the Android operating system. The results of experiments conducted on a commercial big.LITTLE smartphone with real-world mobile apps demonstrate that the proposed design can achieve significant gains in energy efficiency while improving the quality of user experience.
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We address a challenging unsupervised domain adaptation problem with imbalanced cross-domain data. For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain. However, most existing works do not consider the scenarios in which either the label numbers across domains are different, or the data in the source and/or target domains might be collected from multiple datasets. To address the aforementioned settings of imbalanced cross-domain data, we propose Closest Common Space Learning (CCSL) for associating such data with the capability of preserving label and structural information within and across domains. Experiments on multiple crossdomain visual classification tasks confirm that our method performs favorably against state-of-the-art approaches, especially when imbalanced cross-domain data are presented.
An increasing number of mobile devices are being equipped with 802.11n interfaces to support bandwidth-intensive applications; however, the improved bandwidth increases power consumption. To address the issue, researchers are focusing on antenna management. In this paper, we present a dynamic antenna management (DAM) scheme to improve the uplink energy efficiency on mobile devices whose packet workloads may vary significantly and frequently. First, we model antenna management as an optimization problem, with the objective of minimizing the energy required to transmit a sequence of variable-length packets with random arrival times. Then, we propose an optimal offline algorithm to solve the problem, as well as a competitive online algorithm that has a provable performance guarantee and allows compatible implementations on 802.11n mobile devices. To evaluate our scheme, we conducted extensive simulations based on real mobile user traces and application transmission patterns. Nearly all commercial 802.11n mobile devices support the power save mode (PSM). Our results demonstrate that DAM can improve the energy efficiency of PSM significantly at a cost of slight throughput degradation.
The goal of multiple foreground cosegmentation (MFC) is to extract a finite number of foreground objects from an input image collection, while only an unknown subset of such objects is presented in each image. In this paper, we propose a novel unsupervised framework for decomposing MFC into three distinct yet mutually related tasks: image segmentation, segment matching, and figure/ground (F/G) assignment. By our decomposition, image segments sharing similar visual appearances will be identified as foreground objects (or their parts), and these segments will be also separated from background regions. To relate the decomposed outputs for discovering high-level object information, we construct foreground object hypotheses, which allows us to determine the foreground objects in each individual image without any user interaction, the use of pre-trained classifiers, or the prior knowledge of foreground object numbers. In our experiments, we first evaluate our proposed decomposition approach on the iCoseg dataset for single foreground cosegmentation. Empirical results on the FlickrMFC dataset will further verify the effectiveness of our approach for MFC problems.
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With the goal of improving image aesthetics, we propose a novel approach for performing joint recomposition and retargeting of photographic images (R2P). Based on a reference image of interest, we aim at automatically altering the composition of the input source image, while the recomposed output will be jointly retargeted to fit the reference accordingly. Different from existing approaches for improving image aesthetics, our approach requires only one source-reference image pair and does not assume particular aesthetics rules. By extracting the foreground regions using saliency cues, our R2P recomposes the visual components of the source image based on those of the reference by graph matching. The final recomposed and retargeted output is produced by solving a constrained mesh-warping based optimization task, which makes the final output fit the reference image while suppressing possible distortion. In our experiments, we provide qualitative evaluation and comparisons on a wide range of images. We confirm that our method is able to achieve visually satisfactory results, without the need to use pre-collected labeled data or predetermined aesthetics rules.